



import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 					_snake_case      (       lowercase__     ,						lowercase__     ,						unittest.TestCase):
				UpperCamelCase__					:       str  		=StableDiffusionDiffEditPipeline
				UpperCamelCase__					:       Optional[Any]  		=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
				UpperCamelCase__					:       Any  		=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
				UpperCamelCase__					:       Dict  		=frozenset(
				    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				UpperCamelCase__					:       Union[str, Any]  		=frozenset([])




				def 				A__   (    self       :    List[str]    ):
							torch.manual_seed(0    )
							lowercase__       	=					UNetaDConditionModel(
							    block_out_channels=(32, 64),       layers_per_block=2,       sample_size=32,       in_channels=4,       out_channels=4,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),       cross_attention_dim=32,       attention_head_dim=(2, 4),       use_linear_projection=lowercase_,       )
							lowercase__       	=					DDIMScheduler(
							    beta_start=0.00085,       beta_end=0.012,       beta_schedule="scaled_linear",       clip_sample=lowercase_,       set_alpha_to_one=lowercase_,       )
							lowercase__       	=					DDIMInverseScheduler(
							    beta_start=0.00085,       beta_end=0.012,       beta_schedule="scaled_linear",       clip_sample=lowercase_,       set_alpha_to_zero=lowercase_,       )
							torch.manual_seed(0    )
							lowercase__       	=					AutoencoderKL(
							    block_out_channels=[32, 64],       in_channels=3,       out_channels=3,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],       latent_channels=4,       sample_size=128,       )
							torch.manual_seed(0    )
							lowercase__       	=					CLIPTextConfig(
							    bos_token_id=0,       eos_token_id=2,       hidden_size=32,       intermediate_size=37,       layer_norm_eps=1e-0_5,       num_attention_heads=4,       num_hidden_layers=5,       pad_token_id=1,       vocab_size=1000,       hidden_act="gelu",       projection_dim=512,       )
							lowercase__       	=					CLIPTextModel(lowercase_    )
							lowercase__       	=					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

							lowercase__       	=					{
							    "unet": unet,
							    "scheduler": scheduler,
							    "inverse_scheduler": inverse_scheduler,
							    "vae": vae,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "safety_checker": None,
							    "feature_extractor": None,
							}

							return components




				def 				A__   (    self       :    str,       __lowercase       :    Optional[Any],       __lowercase       :    Optional[int]=0    ):
							lowercase__       	=					floats_tensor((1, 16, 16),       rng=random.Random(lowercase_    )    ).to(lowercase_    )
							lowercase__       	=					floats_tensor((1, 2, 4, 16, 16),       rng=random.Random(lowercase_    )    ).to(lowercase_    )
							if str(lowercase_    ).startswith("mps"    ):
										lowercase__       	=					torch.manual_seed(lowercase_    )
							else:
										lowercase__       	=					torch.Generator(device=lowercase_    ).manual_seed(lowercase_    )
							lowercase__       	=					{
							    "prompt": "a dog and a newt",
							    "mask_image": mask,
							    "image_latents": latents,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "inpaint_strength": 1.0,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}

							return inputs




				def 				A__   (    self       :    List[Any],       __lowercase       :    int,       __lowercase       :    Tuple=0    ):
							lowercase__       	=					floats_tensor((1, 3, 32, 32),       rng=random.Random(lowercase_    )    ).to(lowercase_    )
							lowercase__       	=					image.cpu().permute(0,       2,       3,       1    )[0]
							lowercase__       	=					Image.fromarray(np.uinta(lowercase_    )    ).convert("RGB"    )
							if str(lowercase_    ).startswith("mps"    ):
										lowercase__       	=					torch.manual_seed(lowercase_    )
							else:
										lowercase__       	=					torch.Generator(device=lowercase_    ).manual_seed(lowercase_    )
							lowercase__       	=					{
							    "image": image,
							    "source_prompt": "a cat and a frog",
							    "target_prompt": "a dog and a newt",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "num_maps_per_mask": 2,
							    "mask_encode_strength": 1.0,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}

							return inputs




				def 				A__   (    self       :    Optional[int],       __lowercase       :    int,       __lowercase       :    List[str]=0    ):
							lowercase__       	=					floats_tensor((1, 3, 32, 32),       rng=random.Random(lowercase_    )    ).to(lowercase_    )
							lowercase__       	=					image.cpu().permute(0,       2,       3,       1    )[0]
							lowercase__       	=					Image.fromarray(np.uinta(lowercase_    )    ).convert("RGB"    )
							if str(lowercase_    ).startswith("mps"    ):
										lowercase__       	=					torch.manual_seed(lowercase_    )
							else:
										lowercase__       	=					torch.Generator(device=lowercase_    ).manual_seed(lowercase_    )
							lowercase__       	=					{
							    "image": image,
							    "prompt": "a cat and a frog",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "inpaint_strength": 1.0,
							    "guidance_scale": 6.0,
							    "decode_latents": True,
							    "output_type": "numpy",
							}
							return inputs




				def 				A__   (    self       :    Optional[Any]    ):







							if not hasattr(self.pipeline_class,       "_optional_components"    ):
										return

							lowercase__       	=					self.get_dummy_components()
							lowercase__       	=					self.pipeline_class(**lowercase_    )
							pipe.to(lowercase_    )
							pipe.set_progress_bar_config(disable=lowercase_    )

							# set all optional components to None and update pipeline config accordingly
							for optional_component in pipe._optional_components:
										setattr(lowercase_,       lowercase_,       lowercase_    )
							pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}    )

							lowercase__       	=					self.get_dummy_inputs(lowercase_    )
							lowercase__       	=					pipe(**lowercase_    )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(lowercase_    )
										lowercase__       	=					self.pipeline_class.from_pretrained(lowercase_    )
										pipe_loaded.to(lowercase_    )
										pipe_loaded.set_progress_bar_config(disable=lowercase_    )

							for optional_component in pipe._optional_components:
										self.assertTrue(
										    getattr(lowercase_,       lowercase_    ) is None,       F'''`{optional_component}` did not stay set to None after loading.''',       )

							lowercase__       	=					self.get_dummy_inputs(lowercase_    )
							lowercase__       	=					pipe_loaded(**lowercase_    )[0]

							lowercase__       	=					np.abs(output - output_loaded    ).max()
							self.assertLess(lowercase_,       1e-4    )




				def 				A__   (    self       :    str    ):
							lowercase__       	=					"cpu"

							lowercase__       	=					self.get_dummy_components()
							lowercase__       	=					self.pipeline_class(**lowercase_    )
							pipe.to(lowercase_    )
							pipe.set_progress_bar_config(disable=lowercase_    )

							lowercase__       	=					self.get_dummy_mask_inputs(lowercase_    )
							lowercase__       	=					pipe.generate_mask(**lowercase_    )
							lowercase__       	=					mask[0, -3:, -3:]

							self.assertEqual(mask.shape,       (1, 16, 16)    )
							lowercase__       	=					np.array([0] * 9    )
							lowercase__       	=					np.abs(mask_slice.flatten() - expected_slice    ).max()
							self.assertLessEqual(lowercase_,       1e-3    )
							self.assertEqual(mask[0, -3, -4],       0    )




				def 				A__   (    self       :    str    ):
							lowercase__       	=					"cpu"

							lowercase__       	=					self.get_dummy_components()
							lowercase__       	=					self.pipeline_class(**lowercase_    )
							pipe.to(lowercase_    )
							pipe.set_progress_bar_config(disable=lowercase_    )

							lowercase__       	=					self.get_dummy_inversion_inputs(lowercase_    )
							lowercase__       	=					pipe.invert(**lowercase_    ).images
							lowercase__       	=					image[0, -1, -3:, -3:]

							self.assertEqual(image.shape,       (2, 32, 32, 3)    )
							lowercase__       	=					np.array(
							    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799],       )
							lowercase__       	=					np.abs(image_slice.flatten() - expected_slice    ).max()
							self.assertLessEqual(lowercase_,       1e-3    )




				def 				A__   (    self       :    Tuple    ):
							super().test_inference_batch_single_identical(expected_max_diff=5e-3    )




				def 				A__   (    self       :    Tuple    ):
							lowercase__       	=					"cpu"

							lowercase__       	=					self.get_dummy_components()

							lowercase__       	=					{"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
							lowercase__       	=					DPMSolverMultistepScheduler(**lowercase_    )
							lowercase__       	=					DPMSolverMultistepInverseScheduler(**lowercase_    )

							lowercase__       	=					self.pipeline_class(**lowercase_    )
							pipe.to(lowercase_    )
							pipe.set_progress_bar_config(disable=lowercase_    )

							lowercase__       	=					self.get_dummy_inversion_inputs(lowercase_    )
							lowercase__       	=					pipe.invert(**lowercase_    ).images
							lowercase__       	=					image[0, -1, -3:, -3:]

							self.assertEqual(image.shape,       (2, 32, 32, 3)    )
							lowercase__       	=					np.array(
							    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799],       )
							lowercase__       	=					np.abs(image_slice.flatten() - expected_slice    ).max()
							self.assertLessEqual(lowercase_,       1e-3    )
@require_torch_gpu
@slow
class 					_snake_case      (       unittest.TestCase):




				def 				A__   (    self       :    Optional[Any]    ):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				@classmethod
				def 				A__   (    cls       :    Optional[Any]    ):
							lowercase__       	=					load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"    )

							lowercase__       	=					raw_image.convert("RGB"    ).resize((768, 768)    )

							lowercase__       	=					raw_image




				def 				A__   (    self       :    Optional[Any]    ):
							lowercase__       	=					torch.manual_seed(0    )

							lowercase__       	=					StableDiffusionDiffEditPipeline.from_pretrained(
							    "stabilityai/stable-diffusion-2-1",       safety_checker=lowercase_,       torch_dtype=torch.floataa    )
							lowercase__       	=					DDIMScheduler.from_config(pipe.scheduler.config    )
							lowercase__       	=					DDIMInverseScheduler.from_config(pipe.scheduler.config    )
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=lowercase_    )

							lowercase__       	=					"a bowl of fruit"
							lowercase__       	=					"a bowl of pears"

							lowercase__       	=					pipe.generate_mask(
							    image=self.raw_image,       source_prompt=lowercase_,       target_prompt=lowercase_,       generator=lowercase_,       )

							lowercase__       	=					pipe.invert(
							    prompt=lowercase_,       image=self.raw_image,       inpaint_strength=0.7,       generator=lowercase_    ).latents

							lowercase__       	=					pipe(
							    prompt=lowercase_,       mask_image=lowercase_,       image_latents=lowercase_,       generator=lowercase_,       negative_prompt=lowercase_,       inpaint_strength=0.7,       output_type="numpy",       ).images[0]

							lowercase__       	=					(
							    np.array(
							        load_image(
							            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							            "/diffedit/pears.png"    ).resize((768, 768)    )    )
							    / 255
							)
							assert np.abs((expected_image - image).max()    ) < 5e-1




				def 				A__   (    self       :    Tuple    ):
							lowercase__       	=					torch.manual_seed(0    )

							lowercase__       	=					StableDiffusionDiffEditPipeline.from_pretrained(
							    "stabilityai/stable-diffusion-2-1",       safety_checker=lowercase_,       torch_dtype=torch.floataa    )
							lowercase__       	=					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
							lowercase__       	=					DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=lowercase_    )

							lowercase__       	=					"a bowl of fruit"
							lowercase__       	=					"a bowl of pears"

							lowercase__       	=					pipe.generate_mask(
							    image=self.raw_image,       source_prompt=lowercase_,       target_prompt=lowercase_,       generator=lowercase_,       )

							lowercase__       	=					pipe.invert(
							    prompt=lowercase_,       image=self.raw_image,       inpaint_strength=0.7,       generator=lowercase_,       num_inference_steps=25,       ).latents

							lowercase__       	=					pipe(
							    prompt=lowercase_,       mask_image=lowercase_,       image_latents=lowercase_,       generator=lowercase_,       negative_prompt=lowercase_,       inpaint_strength=0.7,       num_inference_steps=25,       output_type="numpy",       ).images[0]

							lowercase__       	=					(
							    np.array(
							        load_image(
							            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							            "/diffedit/pears.png"    ).resize((768, 768)    )    )
							    / 255
							)
							assert np.abs((expected_image - image).max()    ) < 5e-1





from __future__ import annotations

from collections import Counter
from random import random




class 					_UpperCAmelCase			:



 '''simple docstring'''




 def __init__(		self				:   List[str])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   str)						->	None:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   str							, lowercase_				:   str							, lowercase_				:   float)						->	None:


   """simple docstring"""







   if nodea not in self.connections:
     self.add_node(lowercase_)
   if nodea not in self.connections:
     self.add_node(lowercase_)
   _UpperCamelCase           =    probability







 def 		__UpperCAmelCase				(		self				:   Any)						->	list[str]:


   """simple docstring"""







   return list(self.connections)







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   str)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    0
   _UpperCamelCase           =    random()

   for dest in self.connections[node]:
     current_probability += self.connections[node][dest]
     if current_probability > random_value:
       return dest
   return ""


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->dict[str, int]:




  '''simple docstring'''

  _UpperCamelCase           =    MarkovChainGraphUndirectedUnweighted()

  for nodea, nodea, probability in transitions:
    graph.add_transition_probability(a__    ,     a__    ,     a__ )

  _UpperCamelCase           =    Counter(graph.get_nodes() )
  _UpperCamelCase           =    start

  for _ in range(a__ ):
    _UpperCamelCase           =    graph.transition(a__ )
    visited[node] += 1

  return visited


if __name__ == "__main__":
   import doctest

   doctest.testmod()




